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A modified Roland-Morris disability scale for the assessment of sciatica.

Min Kim1, Mathew R Guilfoyle, Helen M Seeley

  • 1Department of Neurosurgery, Addenbrooke's Hospital, Hills Road, Cambridge CB2 2QQ, UK.

Acta Neurochirurgica
|May 15, 2010
PubMed
Summary
This summary is machine-generated.

A modified Roland-Morris Disability Scale (RMS-L) better detects sciatica than the original RMS, improving assessment for low back pain patients. This enhanced tool aids in surgical suitability and outcome prediction.

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Area of Science:

  • Orthopedics
  • Neurosurgery
  • Pain Management

Background:

  • The Roland-Morris Disability Scale (RMS) is widely used for low back pain disability.
  • A modified version, RMS-L, was developed to enhance sensitivity to sciatica.
  • Validation was performed in patients with lumbar disc prolapse and radiculopathy.

Purpose of the Study:

  • To validate the modified RMS-L for assessing disability in patients with lumbar radiculopathy.
  • To compare the sensitivity of RMS-L and the original RMS in detecting sciatica.
  • To evaluate the correlation of both scales with generic health outcome measures.

Main Methods:

  • Prospective administration of RMS and RMS-L to 203 patients with lumbar radiculopathy and disc prolapse.
  • Comparison of scale scores with Visual Analogue Scores (VAS) for leg and back pain.
  • Correlation analysis with Short Form 36 (SF-36) physical function and bodily pain domains at baseline and follow-up.

Main Results:

  • RMS-L showed significantly greater correlation with VAS leg pain than RMS (pre-op and 3-month follow-up).
  • RMS demonstrated higher correlation with VAS back pain compared to RMS-L.
  • RMS-L correlated better with SF-36 physical function and bodily pain, with larger effect sizes.

Conclusions:

  • The modified RMS-L effectively discriminates disability due to sciatica.
  • The original RMS is suitable for assessing low back pain disability.
  • Separate assessment of back pain and sciatica is valuable for surgical decision-making and outcome prediction.